arXiv:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.
By Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu
La‑Ribo is a generative framework that jointly designs RNA nucleotide sequences and their three‑dimensional structures using geometry‑latent flow matching. It preserves a sparse phosphate‑sugar‑base scaffold while encoding nucleotide identity and local conformation in residue‑wise latent variables, which a shared flow network generates before an RNA‑specific decoder reconstructs all heavy atoms. The authors also built a large, quality‑controlled corpus of 168,561 RNA structures—combining experimental data with predictions from three folding models—to expand supervision, and demonstrate that La‑Ribo outperforms baselines in designability and co‑designability across sampling budgets and refolding models, while also enabling scaffold‑conditioned inverse folding without extra training.
By Runze Ma, Will Hua, Shuangjia Zheng
The paper introduces GOLD, a Geometric Optimized Latent Diffusion framework that jointly models RNA sequences and their side‑chain geometries in a unified latent space using a novel GeoVAE. By co‑diffusing sequence and structure and employing a gradient‑guided sampling step that enforces physical constraints, GOLD achieves superior performance in sequence recovery, secondary structure validity, and 3D foldability compared to existing inverse‑folding methods.
By Qi Si, Xuyang Liu, Penglei Wang, Shuo Su, Limei Han, Mengzhu Li, Xin Guo, Yuan Qi, Yuan Cheng
Eco‑RNA is an 80‑million‑parameter RNA foundation model that introduces adjustable inference depth via learned iterative refinement. The shared, input‑conditioned Transformer core repeatedly refines sequence representations, with the number of iterations chosen after training to control effective depth. On the RNAGym fitness benchmark, increasing iterations from one to four improves Spearman correlation from 0.242 to 0.286, and at four iterations Eco‑RNA outperforms the 40‑billion‑parameter Evo 2 model across all aggregate fitness metrics, while also excelling in non‑coding RNA classification, secondary‑structure prediction, and chemical probing profile prediction.
By Pengan Chen, Liang Hong, Xinyuan Liu, Han Huang, Ziqian Lin, Jiayang Chen, Yimin Fan, Jiyue Jiang, Yu Li
arXiv:2609.36885v1 Announce Type: cross
Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or...
By Zefeng Lin, Xianyong Fang, Tianfan Fu, Xiaohua Xu
arXiv:2607. 15309v1 Announce Type: cross Abstract: Proteins function through coordinated motion across multiple spatial and temporal scales, underpinning processes such as ligand binding, allostery, and catalysis.
By Kaihui Cheng, Zhiqiang Cai, Peng Tu, Yisong Yao, Limei Han, Libo Wu, Siyu Zhu, Tzuhsiung Yang, Yuan Qi